Abstract
The Lala copper area in Huili County, Sichuan Province, China, is favored by superior regional metallogenic geological conditions due to its location in an extremely important copper–iron metallogenic belt in southwest China, and it has witnessed the formation of a series of unique iron–copper deposits following the superposition of multiple tectonic events. In recent years, major mineral exploration breakthroughs have been achieved in the deep and peripheral zones of this area. Using the Lala copper mining area in Sichuan as an example, this paper describes metallogenic prediction research carried out based on multivariate geoscience information (geological information, geophysics, geochemistry, and remote sensing data) and the application of geographic information system (GIS) technology and the radial basis function neural network (RBFLN) model. The five specific aspects covered in this paper are as follows: (1) we collected geology–geophysics–geochemistry remote sensing data and other information, adopted GIS technology to extract multivariate geoscience ore-forming anomaly information, and established a geoscience prospecting information database; (2) we applied the RBFLN algorithm for information on integrated analysis of ore-forming anomalies in the study area; (3) we applied a statistical method to divide the threshold value to delineate favorable ore-prospecting target areas; (4) we applied three-dimensional (3D) visualization technology, through which sample assistance was verified, to evaluate the performance of the RBFLN model; and (5) the results revealed that the RBFLN model can integrate multivariate and multi-type geoscience information and effectively predict metallogenic prospective areas and delineate favorable target areas. The metallogenic prediction method based on RBFLN technology provides a scientific basis for the exploration and deployment of minerals in the study area. It is obvious that the methods to predict and evaluate mineral resources are developing towards model integration and information intelligent analysis.
Highlights
Introduction published maps and institutional affilMineral resources play a significant role in China’s economic development
Based on the metallogenic geological conditions and the metallogenic model of the Lala copper concentration area in Sichuan (Figure 1), this paper describes how multivariate geoscience ore-forming information was integrated with the help of geographic information system (GIS) technology and the use of the radial basis function neural network (RBFLN) model
We adopted the RBFLN algorithm to conduct the metallogenic prediction of the Lala ore concentration area in Sichuan Province
Summary
Platform which extends from north to south, and north of the the Yunnan north of. 2). Huili–Eastern Sichuan Aulacogen, which stretches from east to west (Figure 2). Except for the Ordovician–Carboniferous strata, all other strata can be here, with those of the Erathem and the Mesozoic. Stratigraphic combinations in this mainly region cover mainlythe cover the Pre-Sinian. Lower parts of the Luodang Subgroup of the Hekou Group. This region is typical of frequent and intense magmatic activities, which feature. Due to its unique tectonic–magmatic conditions and relatively develdeveloped fluid activities, this region is abundant in metal minerals [24]. Oped fluid activities, this region is abundant in metal minerals [24].
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